Now, I myself have used the word "derp" quite a lot. Possibly more than any other pundit I know, with the exception of Dave Weigel. But in any case, not only do I consider myself an expert in the use of "derp", I also have a very precise idea of what "derp" means, and how it should be used. I think "derp" is incredibly useful as a term for an important concept for which the English language has no other word.

It has to do with Bayesian probability.

Bayesian probability basically says that "probability" is, to some degree, subjective. It's your best guess for how likely something is. But to be Bayesian, your "best guess" must take the observable evidence into account. Updating your beliefs by looking at the outside world is called "Bayesian inference". Your initial guess about the probability is called your "prior belief", or just your "prior" for short. Your final guess, after you look at the evidence, is called your "posterior." The observable evidence is what changes your prior into your posterior.

How much does the evidence change your belief? That depends on three things. It depends on A) how different the evidence is from your prior, B) how strong the evidence is, and C) how strong your prior is.

What does it mean for a prior to be "strong"? It means you really, really believe something to be true. If your start off with a very strong prior, even solid evidence to the contrary won't change your mind. In other words, your posterior will come directly from your prior. (And where do priors come from? On this, Bayesian theory is silent. Let's assume they come directly from your...um...posterior.)

There are many people who have very strong priors about things. For example, there are people who believe, very strongly, that solar power will never be cost-efficient. If you confront them with evidence of solar's rapid price declines, they will continue to insist that, despite this evidence, solar will simply never be cost-competitive with fossil fuels. That they continue to insist this does not necessarily make them irrational in the Bayesian sense; they simply have very strong priors. Someday they may be convinced - for example, if and when unsubsidized solar power starts being adopted on a mass scale. It'll just take a LOT to convince them. (A more entertaining example can be seen in this classic comedy video.)

But here's the thing: When those people keep broadcasting their priors to the world again and again after every new piece of evidence comes out, it gets very annoying. After every article comes out about a new solar technology breakthrough, or a new cost drop, they'll just repeat "Solar will never be cost-competitive." That is unhelpful and uninformative, since they're just restating their priors over and over. Thus, it is annoying. Guys, we know what you think already.

English has no word for "the constant, repetitive reiteration of strong priors". Yet it is a well-known phenomenon in the world of punditry, debate, and public affairs. On Twitter, we call it "derp".

So "derp" is a unique and useful English word. Let's keep using it.

(Also, the verb associated with "derp" is "herp". It describes the action of coughing a large sticky mass of derp onto the internet in front of you. For example, to use it in a sentence: "That twerp just herped a flerp of derp!" A "flerp" is a unit I made up. It is the amount of derp that can be herped by one twerp. See?)

Maybe herping derp is an example that violates the law of diminishing marginal returns (DMR). Your prior is that DMR is always true, however, the evidence is that twerps receive greater marginal returns per flerp the more derp they herp. Since the evidence is infinite and self evident, no prior belief, no matter how strongly held, can withstand it.

The essence of the Liberal outlook lies not in what opinions are held, but in how they are held: instead of being held dogmatically, they are held tentatively, and with a consciousness that new evidence may at any moment lead to their abandonment. Bertrand Russell

This is really hilarious stuff man!Beside being annoying, "derpism" is dangerous too!Anyone who is not willing to reconsider his or prior (no matter how strong they are) in the face of new evidence is simply a fanatic who, if given the chance, could do more harm than good.

Sadly, although I have a reasonable understanding of Bayesian probability, like Blue Aurora above I had no idea what a "derp" was before reading this.

Great article, thanks. One (very minor) quibble: you say "That is unhelpful and uninformative, since they're just restating their priors over and over." Technically, they are stating their posterior - it's just very similar to their prior. But, hopefully, each new piece of evidence makes another little dent in it.

Of course, some people's prior assumptions may be so strong that the evidence can't change it (see Cromwell's rule)...

ArgosyJones may be joking, but there's a pretty strong history of using the phrase "herp-a-derp" to refer to people with Down's syndrome and other developmental delays: http://www.urbandictionary.com/define.php?term=herp%20derps . If you wouldn't be comfortable using some of the other insults in this vein in polite company, you might want to rethink your usage of derp.

I think your definition is a bit off, since it's perfectly possible to have a high prior *and* be willing to change that prior quickly in the light of new evidence. For example, I assign a high prior to propositions like "there isn't a tiger in my bathroom right now," but am willing to change it in a hurry if I see/hear otherwise.

I don't think probability theory has an accepted term for what you are talking about, though I know Brian Skyrms calls it "resiliency." A probability assignment is resilient to the degree, roughly, that you are unwilling to change it in the light of new evidence (but will instead change your probability assignments on other statements). It's an instance of what philosophers of science call the "Quine-Duhem problem"--you can always hold any to any theory (however crazy) so long as you are willing to changes to other beliefs.

In your example, if you have a high prior for not-tiger (say 0.99), you must assign a correspondingly low probability to seeing a tiger in your bathroom, as p(~tiger) = 1-p(tiger). Now say you see the tiger, call that evidence E. How likely is it that you see the tiger if the tiger is there? Say 0.9. How likely is it that you see the tiger if the tiger isn't there? Say 0.01 (the 1% accounting for sudden psychosis, drugs, etc.). Now, by Bayes' Rule, p(tiger|E) =(p(E|tiger)*p(tiger))/p(E) = (0.9*0.1)/0.11 ~= 0.82. Seem reasonable?

Yep, that's a good way of modeling a simple case. One of way of thinking about what is happening with derping is that (1) they assign super-high probabilities to certain propositions AND (2) they "insulate" these priors from revision by failing to assign high (or low) likelihoods to any particular predictions. So, for example, logical truths (such as "it is either raining right now or not raining right now") will tend to be well insulated (since they don't allow for many precise predictions), whereas beliefs about tigers won't be (since they do allow for predictions). Beliefs about economic theory *ought* to be more similar to the tiger case, but derpers fail to see this.

Of course, another possibility is that the derpers simply have incoherent probability assignments, and thus shouldn't be modeled using Bayesian epistemology at all. But that's no fun.

Is derp then always a hypothesis, a la this Bayesian definition. I.e., it seems there should be a distinction between between values (which are typically strongly held), and beliefs about the way the world is (the "model of" side of Geertz's definition of ideology - in contrast to the "model for").What I am getting at is, some values (or lack thereof) may be repellent but I take it they are not "derp".

For example, there are people who believe, very strongly, that solar power will be cost-efficient. If you confront them with evidence that, according to Todd Woody of the New York Times, "Worldwide, testing labs, developers, financiers and insurers are reporting similar problems and say the $77 billion solar industry is facing a quality crisis just as solar panels are on the verge of widespread adoption," they will continue to insist that, despite this evidence, solar will soon be cost-competitive with fossil fuels."

In addition, your link is far less positive about solar than you imply, pointing out that fossil fuels are still far cheaper, and that there's no way to store the electricity that solar cells generate.

I agree with what you're saying, but you should have chosen a better example then solar power costs --or at least linked to a better source then that The Week article. I got through reading it, and all I could think of was Disco Stu's linear projection of disco record sales from 1974.

2/ Because a 40-year trend of continued falling costs in solar power is the same as a 2 or 3 year trend of rising disco record sales, right?

The latter is a short-term trend based on fickle consumer tastes. The former is a long-term trend based on improved technology and there are massive, massive incentives for continued investment and innovation — falling energy costs, and clean non-polluting energy!

Obviously there are some potential hurdles and stumbling blocks on the road toward solar energy that is cheaper than fossil fuels. But comparing it to disco record sales is a pretty big herp.

The word "derp" comes from South Park (and BASEketball before that). I think it's a little ridiculous that you "consider [yourself] an expert in the use of 'derp,'" given that your version of derp is quite different from the word Stone and Parker coined. Whereas "derp!" was originally an exclamatory phrase, you use it as a noun. Both uses call to mind similar associations, but they are entirely different parts of speech, and therefore they are used in very different contexts.

What I mean to say is, you're welcome to use derp in this new way--language is meant to change over time, and that's perfectly great-- but before you call yourself an expert in the use of a word, you might as well look in to the other ways that people use that same word.

Sorry this is off topic but previously you have disputed that a problem with macroeconomics today is that accounting logic isn't followed. JKH has a post all about this. http://monetaryrealism.com/the-accounting-quest-of-steve-keen/There hasn't been any input from those with your viewpoint so it is kind of one sided at the moment. It would be great to get your side of the argument.

Just need to say thanks for this post. The stuff on "derp" is interesting to be sure, but I had been searching for a simple, non-jargon-intensive explanation of just WTF Bayesian analysis is, exactly -- and had given up. Now I've got one.

That's not quite propaganda, which is presentation of only one side of argument. "Black propaganda" is maybe what you're descrihing, according to Wikipedia (yeah I know, Wikipedia). I like your derp definition and Bayesian explanation.

Have you just restated a prior or presented only one side of the argument? :-)

Just as you presented some requirements for predictions, allow me to suggest a requirement for a claim that propaganda is not the same as derp: at the least you should make a Venn diagram of characteristics of propaganda, of derp, of both and neither. With such a model, we could have a better discussion.

Love the post & thanks for the info. However, your definition of the Flerp - "the amount of derp that can be herped by one twerp" - could be improved by adding a temporal dimension, and the Friedman Unit would be the most appropriate denominator.

But what would the numerator be? Perhaps "MeMes" - (pronounced "Me! Me!") to undermine the impressive aspect of "meme" as a measure of meaning, reminding the audience that the derp the twerp herped contains no real meaning beyond a cry for attention.

But the average twerp could probably produce an awful lot of MeMes per Friedman unit, so the Flerp is kinda like the Tesla - a unit which suffers from being rather too large for daily use (unless you study solar flares or run an MRI clinic). But this makes it (the Flerp) even more useful rhetorically, when one can accuse a twerp of herping a whole flerp of derp in 120 charaters or le

I'd have to think Bayesian statisticians and econometricians would think that the prior belief should come from an intelligent logical evaluation of the prior data, information, evidence. It shouldn't be a dogmatic.

My adviser was a successful Bayesian, Chris Lamereoux. Once in a conversation he summed it up well; any good statistician will consider the prior, and other evidence outside of the current study, in an intelligent way in coming up with final beliefs, but Bayesians do so in a very formal way. But he agreed with me that that formality can be a straight jacket that can lead to a less accurate intelligent conclusion than a more flexible analysis and inclusion of your other evidence.

There was a comment (by Nate Silver? Krugman? A poli sci blogger?) that one advantage of formal models is that they force you to state your assumptions explicitly (being aware, of course, about implicit zeros from unconsidered factors).

As has been repeatedly pointed out, many frequentist procedures are equivalent to certain Bayesian procedures, given certain priors. This means that non-Bayesians are doing Bayesian-equivalent work, but without acknowledging or examining priors.

Strong priors do at times make sense. Say you go to a magic show, and the magician is very impressive and you don't have an idea how they did most of the tricks. Do you assume that everything you know about physics is wrong, or do you consider the possibility that Penn and Teller are just very good at their job?

Note, I saw a couple of episodes of a TV show where magicians tried to fool Penn and Teller, as in showing them a trick and the two tried to figure out how it was done, and a few managed to fool Penn and Teller themselves.

Bayesian probability is a form of pseudo-science, as any with a formal education in mathematics should know, I say this because even though I find your post hilarious... I also find you constantly invoke Bayes.

Probability is a just a subset of measure theory, in particular, let F be a sigma algebra on a non-empty set S, and Pr a valued function on F, then Pr is a probability measure on F if and only if

i) Pr is non-negative real valued function on F.ii) Pr is completely additive in F (for any countably infinite collection of pairwise disjoint sets in F, the probability of their union equals the sum of their probabilities).iii) Pr is normed (Pr(S)=1).

Obviously we can form state spaces and so on using the correct definition of the mathematical meaning of probability, and it's completely impossible to force a Bayesian read of this (there's no enough variables to represent the nonsense, even if you assume that the domain is a collection of mental facts).

I agree. I've been seeing this attitude for years and I generally just call it "dishonesty." That's imprecise because lots of these people are also/instead stupid or ensconced in a world where outside criticism never makes it in (watching Fox all day, for example).

Actually in World of Tanks I drive a KV-2 with a 152 mm Derp Cannon. And I love Derp. I once killed 9 tanks with the Derp

from the Wot wiki- "Derp Gun - A gun that causes a lot of damage with one shot, usually having a very long reload time and low penetration. Usually associated with short, High-caliber guns that load HE. Arty's guns are not considered derps (e.g. the 'derp gun' on the USSR tank KV-2)"

"(And where do priors come from? On this, Bayesian theory is silent. Let's assume they come directly from your...um...posterior.)"

Is it really silent? My understanding is that, as a field of study, it's clamorously noisy on that subject. The first (rather obvious) suggestion would be that we adopt as our priors all the previously established body of knowledge discovered by science. That way, if we see a rock falling upward, we can with some confidence rule out a lot of silly ideas to do with telekinesis, and instead home in on things like (a) a tornado in the vicinity, (b) it's not really a rock, (c) you imagined it, etc. If you want to find the objective truth, you need priors to guard you against taking a single freak result too seriously - they give appropriate weight to the previous mountain of evidence.

It was neat wordplay, but there's a possible implication here that you suspect all the previously established body of knowledge discovered by science was just pulled out of someone's ass, or else that you think I'd be no worse off if I pulled some alternative beliefs out of my own. And I'm fairly sure you don't believe that!

The problem with derp is not that these commentators have strong priors, but that they are simply not doing anything like "updating" of anything recognizably based in the hard-won established scientific knowledge.

They started with what they *wished* to be true, and now, whatever happens, they continue to cling to their comforting, fluffy myth-blanket, regardless. Just like religious folks. Not remotely related to Bayesian inference.

Unfortunately, the word "Bayesian" has been used as a talisman by certain internet pundits to explain why hypotheses they don't like can't be true no matter what data support them. If the guy doing the assignment of prior probabilities insists, for example, that a non-Western medical treatment is as unlikely to be effective as a rock is to fall upward, then a dozen positive clinical trials can still leave him virtually certain that all of the actual *science* related to the hypothesis is wrong and should be rejected out of hand. A statistician might call him a Dunning-Kruger case, but he's an extreme example of the overall truth that most of the hypotheses we ponder involve subjects far less certain than gravity, and even if there has been some formal scientific study of the subject, one's evaluation of its meaning and strength will always be influenced by one's personal beliefs. Any estimation of the strength of new evidence for a hypothesis will be equally influenced by ideology - so if I like a study's results, I will say that p<.01 is very strong evidence, while if you don't like them, you will find some reason to call the study "flawed", "weak", or even "worthless". So I wonder if "Bayesian" talk without attached and fully justified numbers is not simply a form of pseudoquantification designed to intimidate one's ideological opponents.

There is also, by the way, the question of what kinds of evidence qualify as evidence at all. You suggest that priors might be derived from all previous "knowledge discovered by science." This opens up a can of worms regarding, at least, the treatment of knowledge not discovered by formal science. In my opinion, the proposition that rocks do not fly was adequately supported far earlier and more strongly by a couple hundred thousand years of human experience, involving literally trillions of man-hours of observation of rocks in their natural habitats, than by the relatively limited and theoretical scientific studies of the matter. If we presume that it is correct to reject the evidence of one's eyes in the case of a particular flying-rock sighting today, it would also have been correct to do so in ancient Rome on the basis of general understanding regarding the behavior of rocks. You certainly did not suggest that you think persons in ancient Rome or in nonliterate traditional cultures today could not claim to know that rocks don't fly - but there are others out there on the Net who imply just that, with some serious and in my opinion unsupportable epistemological implications.

The resistance or transition time to overcome strong priors may perhaps be reduced by smart implementations of at least two mechanisms: 1. an integrated 'incentivization' mechanism such as +/- reps, shares, and re-tweets and properly feeding that back into to users view. 2. A 'credibility' badge overlay (also in the form of an incentivization element) that rewards users who have exhibited the capacity to overcome strong priors. We've seen evidence that both can be effective if properly utilized. In addition, we are developing technologies to implement these techniques which will ultimately be used to 'measure' the notions of credibility and truth of people in online discussions.

This is a great explanation for how internet pundits think about Bayesian inference. This is also terrible explanation of Bayesian inference.

What you're describing is closer to Bayesian updating, and even then, you're not describing it very well. (For one, because Bayesian updating rules are pretty tricky, not widely accepted, and often hard to compute, unstable or undefined.)

Useful jargon, as would be analogous: true believer and stupid ( there are many types of stupid, however). Derp seems another descriptor. The term "normalcy bias" works, maybe, to understand the cause of derp, but derp is more fun.

These are Cipolla's five fundamental laws of stupidity:

Always and inevitably each of us underestimates the number of stupid individuals in circulation.The probability that a given person is stupid is independent of any other characteristic possessed by that person.A person is stupid if they cause damage to another person or group of people without experiencing personal gain, or even worse causing damage to themselves in the process.Non-stupid people always underestimate the harmful potential of stupid people; they constantly forget that at any time anywhere, and in any circumstance, dealing with or associating themselves with stupid individuals invariably constitutes a costly error.A stupid person is the most dangerous type of person there is.